






"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__lowerCamelCase							=  logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCamelCase							=  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    List[Any]=8  )    ->							Any:
						__magic_name__:	List[str]							=       height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__magic_name__:	Any							=       width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



def   a    (     __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Any=5_1_2      ,   __UpperCAmelCase      :    str=5_1_2  )    ->							int:
						__magic_name__:	int							=       pil_image.resize((w, h)      ,   resample=Image.BICUBIC      ,   reducing_gap=1  )
						__magic_name__:	str							=       np.array(pil_image.convert("""RGB"""  )  )
						__magic_name__:	str							=       arr.astype(np.floataa  ) / 1_27.5 - 1
						__magic_name__:	Dict							=       np.transpose(__UpperCAmelCase      ,   [2, 0, 1]  )
						__magic_name__:	Union[str, Any]							=       torch.from_numpy(__UpperCAmelCase  ).unsqueeze(0  )
						return image



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      List[str]     ,		__snake_case  :      UNetaDConditionModel     ,		__snake_case  :      DDPMScheduler     ,		__snake_case  :      VQModel     ,		)			->							List[str]:
											super().__init__()

											self.register_modules(
											    unet=__snake_case     ,		scheduler=__snake_case     ,		movq=__snake_case     ,		)
											__magic_name__:	Tuple							=       2 ** (len(self.movq.config.block_out_channels					) - 1)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[Any]     ,		__snake_case  :      str     ,		__snake_case  :      Any					)			->							Optional[Any]:
											# get the original timestep using init_timestep
											__magic_name__:	str							=       min(int(num_inference_steps * strength					)     ,		__snake_case					)

											__magic_name__:	Dict							=       max(num_inference_steps - init_timestep     ,		0					)
											__magic_name__:	Tuple							=       self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Tuple     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Optional[Any]=None					)			->							Optional[int]:
											if not isinstance(__snake_case     ,		(torch.Tensor, PIL.Image.Image, list)					):
																	raise ValueError(
																	    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case					)}'					)

											__magic_name__:	Union[str, Any]							=       image.to(device=__snake_case     ,		dtype=__snake_case					)

											__magic_name__:	List[str]							=       batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																	__magic_name__:	Dict							=       image

											else:
																	if isinstance(__snake_case     ,		__snake_case					) and len(__snake_case					) != batch_size:
																							raise ValueError(
																							    F'You have passed a list of generators of length {len(__snake_case					)}, but requested an effective batch'
																							    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

																	elif isinstance(__snake_case     ,		__snake_case					):
																							__magic_name__:	str							=       [
																							    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(__snake_case					)
																							]
																							__magic_name__:	List[Any]							=       torch.cat(__snake_case     ,		dim=0					)
																	else:
																							__magic_name__:	Any							=       self.movq.encode(__snake_case					).latent_dist.sample(__snake_case					)

																	__magic_name__:	str							=       self.movq.config.scaling_factor * init_latents

											__magic_name__:	Optional[Any]							=       torch.cat([init_latents]     ,		dim=0					)

											__magic_name__:	Optional[Any]							=       init_latents.shape
											__magic_name__:	Dict							=       randn_tensor(__snake_case     ,		generator=__snake_case     ,		device=__snake_case     ,		dtype=__snake_case					)

											# get latents
											__magic_name__:	Optional[Any]							=       self.scheduler.add_noise(__snake_case     ,		__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[Any]							=       init_latents

											return latents

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Tuple=0					)			->							Tuple:
											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

											__magic_name__:	str							=       torch.device(F'cuda:{gpu_id}'					)

											__magic_name__:	List[str]							=       [
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																							cpu_offload(__snake_case     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Optional[Any]=0					)			->							Any:
											if is_accelerate_available() and is_accelerate_version(""">="""     ,		"""0.17.0.dev0"""					):
																	from accelerate import cpu_offload_with_hook
											else:
																	raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""					)

											__magic_name__:	Any							=       torch.device(F'cuda:{gpu_id}'					)

											if self.device.type != "cpu":
																	self.to("""cpu"""     ,		silence_dtype_warnings=__snake_case					)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											__magic_name__:	Tuple							=       None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	__magic_name__,			__magic_name__:	Any							=       cpu_offload_with_hook(__snake_case     ,		__snake_case     ,		prev_module_hook=__snake_case					)

											# We'll offload the last model manually.
											__magic_name__:	List[Any]							=       hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											if not hasattr(self.unet     ,		"""_hf_hook"""					):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(__snake_case     ,		"""_hf_hook"""					)
																	    and hasattr(module._hf_hook     ,		"""execution_device"""					)
																	    and module._hf_hook.execution_device is not None
																	):
																							return torch.device(module._hf_hook.execution_device					)
											return self.device






					@torch.no_grad()
					@replace_example_docstring(__snake_case					)
					def __call__(			self  :      List[Any]     ,		__snake_case  :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__snake_case  :      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,		__snake_case  :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__snake_case  :      int = 5_1_2     ,		__snake_case  :      int = 5_1_2     ,		__snake_case  :      int = 1_0_0     ,		__snake_case  :      float = 4.0     ,		__snake_case  :      float = 0.3     ,		__snake_case  :      int = 1     ,		__snake_case  :      Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__snake_case  :      Optional[str] = "pil"     ,		__snake_case  :      bool = True     ,		)			->							List[Any]:
											__magic_name__:	Tuple							=       self._execution_device

											__magic_name__:	str							=       guidance_scale > 1.0

											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Optional[int]							=       torch.cat(__snake_case     ,		dim=0					)
											__magic_name__:	Any							=       image_embeds.shape[0]
											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	str							=       torch.cat(__snake_case     ,		dim=0					)

											if do_classifier_free_guidance:
																	__magic_name__:	List[str]							=       image_embeds.repeat_interleave(__snake_case     ,		dim=0					)
																	__magic_name__:	Optional[int]							=       negative_image_embeds.repeat_interleave(__snake_case     ,		dim=0					)

											__magic_name__:	str							=       torch.cat([negative_image_embeds, image_embeds]     ,		dim=0					).to(dtype=self.unet.dtype     ,		device=__snake_case					)

											if not isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Dict							=       [image]
											if not all(isinstance(__snake_case     ,		(PIL.Image.Image, torch.Tensor)					) for i in image					):
																	raise ValueError(
																	    F'Input is in incorrect format: {[type(__snake_case					) for i in image]}. Currently, we only support  PIL image and pytorch tensor'					)

											__magic_name__:	List[Any]							=       torch.cat([prepare_image(__snake_case     ,		__snake_case     ,		__snake_case					) for i in image]     ,		dim=0					)
											__magic_name__:	Optional[Any]							=       image.to(dtype=image_embeds.dtype     ,		device=__snake_case					)

											__magic_name__:	Tuple							=       self.movq.encode(__snake_case					)["""latents"""]
											__magic_name__:	str							=       latents.repeat_interleave(__snake_case     ,		dim=0					)
											self.scheduler.set_timesteps(__snake_case     ,		device=__snake_case					)
											__magic_name__,			__magic_name__:	Tuple							=       self.get_timesteps(__snake_case     ,		__snake_case     ,		__snake_case					)
											__magic_name__:	Tuple							=       timesteps[:1].repeat(batch_size * num_images_per_prompt					)
											__magic_name__,			__magic_name__:	str							=       downscale_height_and_width(__snake_case     ,		__snake_case     ,		self.movq_scale_factor					)
											__magic_name__:	List[Any]							=       self.prepare_latents(
											    __snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		image_embeds.dtype     ,		__snake_case     ,		__snake_case					)
											for i, t in enumerate(self.progress_bar(__snake_case					)					):
																	# expand the latents if we are doing classifier free guidance
																	__magic_name__:	Union[str, Any]							=       torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

																	__magic_name__:	Optional[Any]							=       {"""image_embeds""": image_embeds}
																	__magic_name__:	str							=       self.unet(
																	    sample=__snake_case     ,		timestep=__snake_case     ,		encoder_hidden_states=__snake_case     ,		added_cond_kwargs=__snake_case     ,		return_dict=__snake_case     ,		)[0]

																	if do_classifier_free_guidance:
																							__magic_name__,			__magic_name__:	List[Any]							=       noise_pred.split(latents.shape[1]     ,		dim=1					)
																							__magic_name__,			__magic_name__:	Optional[int]							=       noise_pred.chunk(2					)
																							__magic_name__,			__magic_name__:	str							=       variance_pred.chunk(2					)
																							__magic_name__:	int							=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							__magic_name__:	Optional[Any]							=       torch.cat([noise_pred, variance_pred_text]     ,		dim=1					)

																	if not (
																	    hasattr(self.scheduler.config     ,		"""variance_type"""					)
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																							__magic_name__,			__magic_name__:	str							=       noise_pred.split(latents.shape[1]     ,		dim=1					)

																	# compute the previous noisy sample x_t -> x_t-1
																	__magic_name__:	List[str]							=       self.scheduler.step(
																	    __snake_case     ,		__snake_case     ,		__snake_case     ,		generator=__snake_case     ,		)[0]

											# post-processing
											__magic_name__:	Any							=       self.movq.decode(__snake_case     ,		force_not_quantize=__snake_case					)["""sample"""]

											if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'					)

											if output_type in ["np", "pil"]:
																	__magic_name__:	Any							=       image * 0.5 + 0.5
																	__magic_name__:	int							=       image.clamp(0     ,		1					)
																	__magic_name__:	Union[str, Any]							=       image.cpu().permute(0     ,		2     ,		3     ,		1					).float().numpy()

											if output_type == "pil":
																	__magic_name__:	Dict							=       self.numpy_to_pil(__snake_case					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__snake_case					)




"""simple docstring"""



import os



def 	_lowerCamelCase(	a = "matrix.txt"				):
       with open(os.path.join(os.path.dirname(a				)   ,    a				)				) as in_file:
              __a       =    in_file.read()

       __a       =    [[int(a				) for cell in row.split(","				)] for row in data.strip().splitlines()]
       __a       =    [[0 for cell in row] for row in grid]
       __a       =    len(grid[0]				)

       __a       =    [[0 for i in range(a				)] for j in range(a				)]
       __a       =    grid[0][0]
       for i in range(1   ,    a				):
              __a       =    grid[0][i] + dp[0][i - 1]
       for i in range(1   ,    a				):
              __a       =    grid[i][0] + dp[i - 1][0]

       for i in range(1   ,    a				):
              for j in range(1   ,    a				):
                     __a       =    grid[i][j] + min(dp[i - 1][j]   ,    dp[i][j - 1]				)

       return dp[-1][-1]


if __name__ == "__main__":
   print(F'''{solution() = }''')
